Hazard-based duration modelling of merging time interval on freeway on-ramps

Freeway on-ramps suffer high crash risks due to frequent merging behaviours.This study developed hazard-based duration models to investigate the merging time interval on freeway on-ramps based on microscopic trajectory data.Fixed effect,random effect and random parameters Weibull distributed accelerated failure time models were developed to capture merging time as a function of various dynamic variables.The random parameters model was found to outperform the two counterparts since the unobserved heterogeneity of individual drivers was captured.Modelling estimation results indicate that drivers along the merging section with an auxiliary lane perform a smooth merging process and are easily affected by speed variables.Dynamics of leading and following vehicles on the merging and target lanes are found to influence the merging time interval for merging without an auxiliary lane,whereas the influence of surrounding vehicles ismarginal for thosewith an auxiliary lane.The findings of this study identify potential countermeasures for improving safety during the merging process.

Developing Project Duration Models in Software Engineering

Based on the empirical analysis of data contained in the International Software Benchmarking Standards Group （ISBSG） repository, this paper presents software engineering project duration models based on project effort. Duration models are built for the entire dataset and for subsets of projects developed for personal computer, mid-range and mainframe platforms. Duration models are also constructed for projects requiring fewer than 400 person-hours of effort and for projects requiring more than 400 person-hours of effort. The usefulness of adding the maximum number of assigned resources as a second independent variable to explain duration is also analyzed. The opportunity to build duration models directly from project functional size in function points is investigated as well.

Dew measurement and estimation of rain-fed jujube (Zizyphus jujube Mill) in a semi-arid loess hilly region of China

Dew is an important water source for plants in arid and semi-arid regions. However, information on dew is scarce in such regions. In this study, we explored dew formation, amount, and duration of rain-fed jujube（Zizyphus jujube Mill） trees in a semi-arid loess hilly region of China（i.e., Mizhi County）. The data included dew intensity and duration, relative humidity, temperature, and wind speed measured from 26 July to 23 October, 2012 and from 24 June to 17 October, 2013 using a micro-climate system（including dielectric leaf wetness sensors, VP-3 Relative Humidity/Temperature Sensor, High Resolution Rain Gauge, and Davis Cup Anemometer）. The results show that atmospheric conditions of relative humidity of 〉78% and dew point temperature of 1°C–3°C are significantly favorable to dew formation. Compared with the rainfall, dew was characterized by high frequency, strong stability, and long duration. Furthermore, heavy dew accounted for a large proportion of the total amount. The empirical models（i.e., relative humidity model（RH model） and dew point depression model（DPD model）） for daily dew duration estimation performed well at 15-min intervals, with low errors ranging between 1.29 and 1.60 h, respectively. But it should be noted that the models should be calibrated firstly by determining the optimal thresholds of relatively humidity for RH model and dew point depression for DPD model. For rain-fed jujube trees in the semi-arid loess hilly regions of China, the optimal threshold of relative humidity was 78%, and the optimal upper and lower thresholds of dew point depression were 1°C and 5°C, respectively. The study further demonstrates that dew is an important water resource that cannot be ignored for rain-fed jujube trees and may affect water balance at regional scales.

A predictive accident-duration based decision-making module for rerouting in environments with V2V communication

Communication for cooperative traffic management increases convenience and efficiency in driving. Intelligent vehicles can collect information about the driving environment, the driver situation and, more importantly, information on other vehicles. While giving this information to the driver can be useful, there is also the possibility of presenting the driver with too much information. Existing vehicles already have some mechanisms to take certain actions if the driver fails to act. Future vehicles will need more complex decision-making modules which receive the raw data from all available sources, process this data and inform the driver about existing or impending situations and suggest, or even take actions. In this work, we explore the use of a decision-making module for accident situations that processes information from VANET communication and advises the driver based on the situation. Our decision-making algorithm is a simple and effective algorithm that can be implemented in each vehicle to assist the driver in situations where rerouting to avoid traffic congestion caused by an accident may be necessary. Our approach estimates accident duration and provides a rerouting for upcoming traffic. Our algorithm has been implemented and evalu- ated within an elegant traffic simulation system where both city and highway environments are considered, along with various proportions of V2V-equipped vehicles. Overall, the results indicate that using our decision-making module in vehicles shows great potential for improving performance of vehicular systems by reducing both travel and wait times and providing more accurate information on surrounding environments.

Effect of time-of-day and day-of-the-week on congestion duration and breakdown:A case study at a bottleneck in Salem,NH

This work uses regression models to analyze two characteristics of recurrent congestion： breakdown, the transition from freely flowing conditions to a congested state, and duration, the time between the onset and clearance of recurrent congestion. First, we apply a binary logistic regression model where a continuous measurement for traffic flow and a dichoto- mous categorical variable for time-of-day （AM- or PM-rush hours） is used to predict the probability of breakdown. Second, we apply an ordinary least squares regression model where categorical variables for time-of-day （AM- or PM-rush hours） and day-of-the-week （Monday-Thursday or Friday） are used to predict recurrent congestion duration. Models are fitted to data collected from a bottleneck on 1-93 in Salem, NH, over a period of 9 months. Results from the breakdown model, predict probabilities of recurrent congestion, are consistent with observed traffic and illustrate an upshift in breakdown probabilities between the AM- and PM-rush periods. Results from the regression model for congestion duration reveal the presences of significant interaction between time-of-day and day-of-the-week. Thus, the effect of time-of-day on congestion duration depends on the day-of-the-week. This work provides a simplification of recurrent congestion and recovery, very noisy processes. Simplification, conveying complex relationships with simple statistical summaries-facts, is a practical and powerful tool for traffic administrators to use in the decision-making process.

On interest-rate risk management of postal savings bureau

This article analyzes interest-rate risks faced by the postal savings bureau （PSB） based on the complete balance sheet. It presents the extended gap model and the extended duration gap model to measure the interest-rate risk, and discusses the inner balance-sheet strategies and the off-balance-sheet strategies to manage the interest-rate risks.

Adaptive Compensation Algorithm in Open Vocabulary Mandarin Speaker-Independent Speech Recognition

In speech recognition systems, the physiological characteristics of the speech production model cause the voiced sections of the speech signal to have an attenuation of approximately 20 dB per decade. Many speech recognition algorithms have been developed to solve this problem by filtering the input signal with a single-zero high pass filter. Unfortunately, this technique increases the noise energy at high frequencies above 4 kHz, which in some cases degrades the recognition accuracy. This paper solves the problem using a pre-emphasis filter in the front end of the recognizer. The aim is to develop a modified parameterization approach taking into account the whole energy zone in the spectrum to improve the performance of the existing baseline recognition system in the acoustic phase. The results show that a large vocabulary speaker-independent continuous speech recognition system using this approach has a greatly improved recognition rate.